1. Field of the Invention
The present invention relates to ultra-wideband signal processing, and particularly to a peak detection method using blind source separation.
2. Description of the Related Art
Efficient utilization of radio spectrum has gained recent attention. It has been observed that utilization of spectrum by licensed wireless systems, for instance TV broadcasting, is quite low. Transition from voice only data services to multimedia services requires high data rates. Current static frequency allocation schemes cannot cope forever with increasing data rates. Some frequency bands are overcrowded, and some are barely used. A spectrum occupancy measurement project concluded that the average spectrum occupancy over multiple locations is 5.2%, with a maximum of 13.1%.
Cognitive radio (CR) seems a tempting solution to resolve the perceived bandwidth scarcity versus under-utilization dilemma. CR uses opportunistic usage of bands that are not crowded by licensed users. They use spectrum sensing to sense the frequency bands that are unoccupied by licensed users and transmit on these bands to avoid harmful interference to licensed users.
CRs front end architecture is dependent on spectrum utilization. For spectrum utilization under 20%, a wideband architecture for the CR front end is suggested. The observed spectrum consists of numerous frequency bands. Power spectral density (PSD) within each frequency band is smooth. Transition of PSD from one band to another band is considered as irregularities in PSD. Such irregularities can be studied using wavelet transforms, which are capable of characterizing local regularity of a signal.
Applying a wavelet transform on an incoming signal results in peaks at locations where signal PSD is irregular. Irregularity could be a jump or a fall in PSD, depicting the change of frequency band. Jump depicts that the next user has higher PSD than the current one, whereas fall shows that the next user has lower PSD than the current one, or it could also be a vacant band. When the incoming signal is noisy, these peaks are accompanied by noisy peaks. In some known methods, multiscale wavelet products are used to extract true peak information. This technique requires multiplication of various wavelet transform gradients (for the same signal).
As a result of this, true peaks will be enhanced, whereas random noisy peaks will be suppressed. However this technique requires a priori knowledge regarding the total number of occupied bands in a spectrum at a given instance. This information is normally unknown to CR.
Thus, a peak detection method using blind source separation solving the aforementioned problems is desired.